What comment I can say about this: Example Consider the following scenario: Monthly sales data from 100 stores over two years show that most stores have sales ranging from $20,000 to $50,000. Two stores report monthly sales of $100,000. 1. Visual Inspection: A box plot reveals two distinct outliers far above the upper whisker. 2. Descriptive Statistics: Calculate IQR ($50,000 - $20,000 = $30,000). Values above $35,000 (Q3 + 1.5 * IQR) are outliers. 3. Log Transformation: Applying log transformation reduces the impact of $100,000 values. 4. Winsorizing: Adjust $100,000 values down to $50,000, the next highest non-outlier value. 5. Exclusion: If verified as data entry errors, the two $100,000 values can be excluded. Conclusion Outliers must be handled carefully to ensure accurate statistical analysis. Combining visual inspection, descriptive statistics, robust methods, transformation, Winsorizing, and exclusion can effectively manage outliers, leading to a more reliable interpretation of the data